An Analysis of Student Satisfaction and its Relationship with Academic and Social Factors in University Dropout

This aim of this article is to study the use of Educational Data Mining (EDM) techniques in constructing concept maps for organizing knowledge in web based learning systems whereby studying their synergistic effects in enhancing learning. This article first provides a tutorial based introduction to EDM. The applicability of web based learning systems in enhancing the efficiency of EDM techniques in real time environment is investigated. Web based learning systems often use a tool for organizing knowledge. This article explores the use of one such tool called concept map for this purpose. The pioneering works by various researchers who proposed web based learning systems in personalized and collaborative environment in this arena are next presented. A set of parameters are proposed based on which personalized and collaborative learning applications may be generalized and their performances compared. It is found that personalized learning environment uses EDM techniques more exhaustively compared to collaborative learning for concept map construction in web based environment. This article can be used as a starting point for freshers who would like to use EDM techniques for concept map construction for web based learning purposes.

This study investigates the use of educational data mining (EDM) techniques to predict student performance and enhance learning outcomes in higher education. Leveraging data from Moodle, a widely used learning management system (LMS), we analyzed 450 students’ academic records spanning nine semesters. Five machine learning algorithms—k-nearest neighbors, random forest, logistic regression, decision trees, and neural networks—were applied to identify correlations between courses and predict grades. The results indicated that courses with strong correlations (+0.3 and above) significantly enhanced predictive accuracy, particularly in binary classification tasks. kNN and neural networks emerged as the most robust models, achieving F1 scores exceeding 0.8. These findings underscore the potential of EDM to optimize instructional strategies and support personalized learning pathways. This study offers insights into the effective application of data-driven approaches to improve educational outcomes and foster student success.

The aim of paper is to show the benefits of the educational data mining (EDM) techniques, in order to understand about of the factors which lead to technical student’s success and failure, and predict their performance and determine the individual learning ability in engineering sciences. For these goals, we use the individual data of 311 student and their grades that were collected in Industrial Institute of Al-Diwaniyah city (Iraq) during 2015–2017 academic years, in order to predict the results of final theoretical exam in industrial drawing by applying EDM techniques, such as association rules mining, classification with decision tree algorithm learning, clustering with Apriori algorithm and anomaly detection implemented as the output model of the clustering. Using Microsoft SQL Server Business Intelligence Development Studio 2012 platform and based on Cross Industry Standard Process for Data Mining, we prepare of 13 nominal and numerical attributes for each student and consequently apply and finally evaluate all 4 EDM techniques. We conclude that: 1) association rules were revealed that the most important factor which contribute to the failure of the student is the “project” attribute; 2) decision tree classification permit to the teacher predict the future students and to correct the student's prediction path, but 3) clustering collects of the students into successful and failure groups and helps to the teacher to guide each group separately, and 4) to detect anomaly by аn extension DMX for SQL and correct the education process for students located on the border of the cluster.

An educational dashboard is a display which visualizes the results of educational data mining in a useful way. Educational data mining and visualization techniques allow teachers and students to monitor and reflect on their online teaching and learning behavior patterns. Previous literature has included such information in the dashboard to support students’ self-knowledge, self-evaluation, self-motivation, and social awareness. Further, educational dashboards are expected to support the smart learning environment, in the perspective that students receive personalized and automatically-generated information on a real-time base, by use of the log files in the Learning Management System (LMS). In this study, we reviewed ten case studies that deal with development and evaluation of such a tool, for supporting students and teachers through educational data mining techniques and visualization technologies. In the present study, a conceptual framework based on Few’s principles of dashboard design and Kirkpatrick’s fourlevel evaluation model was developed to review educational dashboards. Ultimately, this study is expected to evaluate the current state of educational dashboard development and suggest an evaluative tool to judge whether or not the dashboard function is working properly, in both a pedagogical and visual way.

This research aims to predict the academic achievement of Pamulang University students using Educational Data Mining (EDM) techniques. With the increasing number of students and the complexity of academic data, it is important to apply methods that can analyze and predict learning outcomes to improve learning strategies and academic support. This study collected data from various sources, including course grades, attendance, and participation in extracurricular activities. The collected data is then analyzed using EDM techniques such as decision trees, neural networks, and support vector machines to identify patterns and factors that contribute to student academic achievement. The results of the analysis show that factors such as attendance, involvement in campus activities, and previous test scores have a significant influence on academic achievement. This research provides valuable insights for the development of targeted interventions, with the aim of improving academic outcomes and facilitating more effective learning strategies at Pamulang University. These findings also offer contributions to further research in the field of EDM and its application in higher education contexts.

Several higher educational institutions are adapting the strategy of predicting the student’s performance throughout the academic years. Such a practice ensures not only better academic outcomes but also helps the institutions to reorient their curriculums and teaching pedagogies so as to add to the students’ learning curve. Educational Data Mining (EDM) has risen as a useful technology in this league. EDM techniques are now being used for predicting the enrolment of students in a specific course, detection of any irregular grades, prediction about students’ performance, analyzing and visualizing of data, and providing feedback for overall improvement in the academic spheres. This paper reviews the studies related to EDM, including the approaches, data sets, tools, and techniques that have been used in those studies, and points out the most efficient techniques. This review paper uses true prediction accuracy as a standard for the comparison of different techniques for each EDM applications of the surveyed literature. The results show that the J48 and K-means are the most effective techniques for predicting the students’ performance. Furthermore, the results also cite that Bayesian and Decision Tree Classifiers are the most widely used techniques for predicting the students’ performance. In addition, this paper highlights that the most widely used tool was WEKA, with approximately 75% frequency. The present study’s empirical assessments would be a significant contribution in the domain of EDM. The comparison of different tools and techniques presented in this study are corroborative and conclusive, thus the results will prove to be an effective reference point for the practitioners in this field. As a much needed technological asset in the present day educational context, the study also suggests that additional surveys are recommended to be driven for each of the EDM applications by taking into account more standards to set the best techniques more accurately.

In the digital era, enormous amounts of data have been generated in education that has led to data driven approaches which in turn help effective decision making. Educational data mining has been used as a very effective tool for identifying the hidden patterns in educational data, predicting students' performance and to enhance the teaching /learning environment. Educational data mining tools and techniques help institutes in providing information about students e.g., about enrolment of students, weak students can be identified earlier so that various corrective strategies can be applied, and various resources can be allocated to enhance their performance and their success in courses they enrolled. This paper examines the research efforts that have been made in the field of educational data mining and the various educational data mining tools and techniques used in recent years for predicting student's performance. We live in a world where enormous volumes of data are collected, but if these data are not further examined, they remain nothing more than enormous amounts of data We may use this data, analyze it, and gain a significant edge by using new approaches and procedures. Data mining is the ideal approach in this situation. Extraction of hidden and valuable information and patterns from massive data sets is known as data mining. It has already been widely used in several fields, including banking, sales, marketing, telecommunications, and finance. This essay aims to introduce a unique use of data mining for education, known as educational data mining. An interdisciplinary study topic called Educational Data Mining (EDM) was established to apply data mining to the educational sector. To examine the data gathered during teaching and learning, it employs a variety of tools and methodologies from machine learning, statistics, data mining, and data analysis. The process of turning large educational databases' raw data into useful information that can be used for decision-making in educational systems as well as for a better understanding of students and their learning circumstances is known as educational data mining.

Massive Open Online Courses (MOOCs) and microcredential have transformed education by providing flexible and accessible learning opportunities. This study conducts a comparative analysis of MOOC platforms, focusing on Coursera and Udemy, using Educational Data Mining (EDM) techniques. The research examines differences in course volume, subject trends, learner engagement, and course structure across platforms. The findings reveal that Coursera offers fewer but more structured and academically rigorous courses, often linked to university and corporate partnerships. Udemy, in contrast, provides a vast number of shorter, skill-focused courses with greater variability in quality due to its open-marketplace model. Coursera’s courses have higher ratings and more consistent engagement, while Udemy follows a winner-takes-most pattern, where a few popular courses dominate learner interest. Text analysis of course titles highlight distinct subject preferences. Both platforms prioritize technology and business topics, but Coursera’s content aligns with long-term career pathways, whereas Udemy quickly adapts to emerging industry trends, such as blockchain and artificial intelligence. The study also identifies differences in learning pathways, with Coursera structuring its content into beginner, intermediate, and advanced levels, while Udemy heavily relies on “all-level” courses to attract a broad audience. These insights provide valuable implications for educators, policymakers, and online learning providers. Structured platforms like Coursera can strengthen their credibility through academic rigor and industry partnerships, while market-driven platforms like Udemy can enhance content discovery and quality control. Future research should explore the role of course pricing, instructor expertise, and microcredential effectiveness in learner success.

The use of advanced learning technologies (ALT) techniques in learning management systems (LMS) allows teachers to enhance self-regulated learning and to carry out the personalized monitoring of their students throughout the teaching–learning process. However, the application of educational data mining (EDM) techniques, such as supervised and unsupervised machine learning, is required to interpret the results of the tracking logs in LMS. The objectives of this work were (1) to determine which of the ALT resources would be the best predictor and the best classifier of learning outcomes, behaviours in LMS, and student satisfaction with teaching; (2) to determine whether the groupings found in the clusters coincide with the students’ group of origin. We worked with a sample of third-year students completing Health Sciences degrees. The results indicate that the combination of ALT resources used predict 31% of learning outcomes, behaviours in the LMS, and student satisfaction. In addition, student access to automatic feedback was the best classifier. Finally, the degree of relationship between the source group and the found cluster was medium (C = 0.61). It is necessary to include ALT resources and the greater automation of EDM techniques in the LMS to facilitate their use by teachers.

Business intelligence (BI) builds upon a set of tools and applications that enable the analysis of vast amounts of information (Big Data). Educational institutions handle large volumes of Big Data every year. There is a strong need for the use of BI in these institutions to improve their processes and support decision making. The core technology in a BI project is a datawarehouse (DW). This paper describes the design considerations for the implementation of the DW in an educational scenario. The DW will be used in a knowledge discovery process to handle the information for the analysis of key performance indicators using educational data mining (EDM) techniques. The DW along with an enterprise architecture (EA) repository are the key technological assets of a knowledge management framework (KMF). This framework was designed to put order in the creation, capture, transfer and digitalization of knowledge. This guide and the framework are two of the outcomes of a research project in a private university. Furthermore, a case study suggests how to choose the best methodology in higher institutions. In the case study the steps for the DW design are presented. This study can be useful for academics and practitioners that plan to design a DW to analyze information using EDM techniques.

This complete article of the research category presentes proposes the application of Educational Data Mining (EDM) techniques, based on data generated by a web-based Intelligent Tutor System (ITS), developed for the teaching of algebra, which helps students to develop fundamental knowledge of mathematics, in addition to carrying out a comparative study between the level of difficulty of the proposed algebraic questions. For this, we use the knowledge discovery methodology to perform the following steps from the data: cleaning, integration, selection, transformation, mining, evaluation and presentation of information. The practical results reveal that the proposed architecture can classify the academic performance of students in each evaluation period with an accuracy of around 80%. It was also possible to identify factors related to the resolution of questions, such as the rate of correct answers and the mathematical steps to solve an exercise, classifying the level of difficulty of the questions proposed by the system, acting on the student’s deficiencies, and the system. The results obtained from the data analysis can serve as a basis for decision-making, helping in the teaching process for teachers, tutors and managers to monitor academic performance, enabling the correction of problems and identifying the individual and collective difficulties present in each class. In summary, these results also provide a general roadmap on the performance of using EDM techniques in a given context.

Predicting students’ performance has emerged as an attractive task among researchers. They use supervised and unsupervised educational data mining (EDM) techniques to build an understandable and effective model. This helps decision makers enhance the performance of the students. The challenge of finding an optimal model leads to appearance of many techniques from both EDM techniques. Hence, we propose a unified framework to build a novel supervised cluster-based (CB) classifier model. The unified framework uses clustering technique to group historical records of students into a set of homogeneous clusters. Then, classifier model for each cluster is built and the final unified classifiers along with the centroids at each cluster are used as CB classifier model. The experimental results show that the CB model gains a high accuracy performance reached 96.25%. In addition, we use feature selection techniques for selecting the relevant features from a space of features. The model obtains a high accuracy performance using relevant features reached to 96.96% where the percentage of relevant features on average is 57.4% of overall features.

Educational data mining and learning analytics for 21st century higher education: A review and synthesis

Predictors of not pursuing infertility treatment after an infertility diagnosis: examination of a prospective U.S. cohort

In the past decade, we have witnessed a tremendous rise in the use of electronic devices in education. Starting from nursery classes at the preschool level to the postgraduate programs at the universities, electronic devices are being used extensively to enhance and facilitate quality of education. Although the use of computer networks is an inherent feature of online learning, the traditional schools and universities are also making extensive use of network-connected electronic devices such as mobile phones, tablets, and computers. However, it is humanly impossible to analyze enormous volume of data generated from the active usage of devices connected through a large network. The educators and academic administrators can benefit from their counterparts in business and service industries where a complex system of methods and techniques, usually referred to as data analytics or data mining, is being used to analyze a large influx of real-time data in decision-making. Researchers have started paying attention to the application of data mining and data analytics to handle big data generated in the educational sector. In the context of education, these techniques are specifically referred to as educational data mining (EDM) and learning analytics (LA). Generally, EDM looks for new patterns in data and develops new algorithms and/or new models, while LA applies known predictive models in instructional systems. This chapter starts by describing major EDM and LA techniques used in handling big data in commercial and other activities. It will also provide a brief description of how EDM and LA are affecting the typical stakeholders of a higher education institution. Furthermore, the chapter will provide a detailed account of how these techniques are used to analyze the learning process of students, assessing their performance and providing them with detailed feedback in real time. These Technologies can also assist in planning administrative strategies to provide quality services to all stakeholders of an educational institution. Not all the stakeholders involved in providing education are experts of big data. However, in order to meet their analytical requirements, the researchers have developed easy-to-use data mining and visualization tools. In this chapter, we have provided the necessary details of some of these tools. The institutions/governments across the world are adopting EDM/LA to frame strategic policies, to understand the students’ learning behaviors, etc. As case studies, we have also discussed some implementation of EDM and LA techniques in universities in different countries.